Emerging data fusion methodology in multisensor integrated navigation for challenging environments

适用于挑战性环境的多传感器集成导航中的新兴数据融合方法

• 批准号: RGPIN-2017-05717
• 负责人: WANG, JIANGUO
• 金额: $ 1.75万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711775
• 关键词: Emerging; data; fusion; methodology; multisensor

With the increasing availability of low-cost sensors (MEMS IMUs, cameras, LiDARs etc.), the challenge is how to optimally integrate them to reach accurate and reliable navigation solution in challenging environments, e.g., GNSS degraded or denied environments. Meanwhile, the increasing demand for high quality multisensor integrated navigation is continually driving more and more research and development activities and applications. Hence, the long-term goal of this research is to develop the emerging data fusion methodology in multisensor integrated navigation for challenging environments. Its focus is placed on innovating multisensor integration strategies in consideration of rapid technological progress and economic productivity, enhancing system accuracy and reliability for the growing needs of seamless indoor and outdoor navigation, and exploring new applications. The long-term research goal will be achieved through fulfilling the proposed short-term objectives. First, the general multisensor integration strategy, developed during the last Discover Grant Program, has made possible for the sensor measurements to directly participate in Kalman filter measurement updates without distinguishing between the core IMU and other aiding sensors by employing a kinematic model of a moving platform as the core of the system model. This is paramount to the utilization of low-cost sensors, e.g., MEMS IMUS. However, for its practicability, this generic multisensor integration strategy will further be improved and optimized through theoretical and practical enhancements such as effective reduction of high rate measurement updates, optimal state selections, smooth transitions between alternative kinematic models, solution evaluation at system level, and error analysis at sensor level. Second, the investigation of the potential of low-cost IMU arrays will be conducted by modeling them individually and developing low-cost IMU auto calibration methodology. Third, we will develop LiDAR-visual odometry to further enhance multisensor (GNSS receivers/IMUs/Cameras) integrated navigation for the better navigation solution in challenging environments, and further a framework for a complementarily-integrated solution of trajectory determination and 3D mapping. This program will greatly contribute to their advancement by its significance of a practically applicable generic multisensor integration strategy, the new approach to model IMU arrays and the low-cost IMU auto calibration process and a framework for a complementarily-integrated solution of trajectory determination and 3D mapping. The success of this research program will greatly impact on meeting the great challenges due to the benefits of low cost sensors and the complication in challenging working environments and improving the lack of highly demanded high-qualified personnel in this area to some extent

随着低成本传感器（MEMS伊穆斯、照相机、激光雷达等）的日益普及，挑战在于如何最佳地集成它们以在具有挑战性的环境中实现准确和可靠的导航解决方案，例如，GNSS降级或拒绝环境。与此同时，对高质量多传感器组合导航的需求不断增长，推动了越来越多的研究和开发活动和应用。因此，本研究的长期目标是发展新兴的数据融合方法在多传感器组合导航的挑战性环境。其重点是考虑到快速的技术进步和经济生产力，创新多传感器集成策略，提高系统的准确性和可靠性，以满足日益增长的无缝室内和室外导航需求，并探索新的应用。 通过实现所提出的短期目标，实现长期研究目标。首先，一般的多传感器集成策略，在过去的发现资助计划，使传感器测量直接参与卡尔曼滤波器测量更新，而不区分核心IMU和其他辅助传感器采用的运动学模型的移动平台作为系统模型的核心。这对于低成本传感器的利用是至关重要的，例如，MEMS伊穆斯系统。然而，对于它的实用性，这种通用的多传感器集成策略将进一步改进和优化，通过理论和实践的增强，如有效地减少高速率的测量更新，最佳状态选择，替代运动学模型之间的平滑过渡，在系统级的解决方案评估，在传感器级的误差分析。其次，将通过单独建模和开发低成本IMU自动校准方法来研究低成本IMU阵列的潜力。第三，我们将开发激光雷达-视觉里程计，以进一步增强多传感器（GNSS接收器/伊穆斯/摄像机）组合导航，在具有挑战性的环境中提供更好的导航解决方案，并进一步建立一个框架，用于轨迹确定和3D映射的互补集成解决方案。 该计划将极大地促进其发展，其意义在于实际适用的通用多传感器集成策略，模型IMU阵列和低成本IMU自动校准过程的新方法，以及轨迹确定和3D映射的互补集成解决方案的框架。该研究项目的成功将极大地影响应对由于低成本传感器的优点和具有挑战性的工作环境的复杂性而带来的巨大挑战，并在一定程度上改善该领域缺乏高素质人才的情况